「db」の検索結果

Documentation and extension workflows derived from local slash commands (docs, explanation, issues, prototypes, tutorials, MCP design review).

日本語の概要は準備中です。原文の説明を表示しています。

Triage TestFlight crashes, beta feedback, and performance diagnostics using asc. Use when the user asks about TF crashes, TestFlight crash reports, beta tester feedback, app hangs, disk writes, launch diagnostics, or wants a crash summary for a build or app.

日本語の概要は準備中です。原文の説明を表示しています。

Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

日本語の概要は準備中です。原文の説明を表示しています。

Post project updates to team chat, gather feedback, triage responses, and plan next steps. Adapts to available tools (chat, git, issues, tasks). First run discovers tools and saves a playbook; subsequent runs execute from the playbook. Trigger with 'team update', 'post update', 'sync with team', 'standup', 'check team chat', 'feedback loop', 'project update', 'what did the team say'.

日本語の概要は準備中です。原文の説明を表示しています。

Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a creative task has no direction yet: finding inspiration, exploring looks or styles, proposing concept directions to pick between, building a moodboard or reference board, doing visual research, mining existing assets for a style, or escaping generic AI-looking output. Also when a saved Scenario collection should drive a new batch as art direction. Keywords: inspiration, moodboard, references, art direction, style exploration, concepts, serendipity, ideation, surprise me.

日本語の概要は準備中です。原文の説明を表示しています。

Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

日本語の概要は準備中です。原文の説明を表示しています。

Assess a field lesson for reuse or product feedback without exposing customer context. Use for recurring deployment lessons; distinguish a hypothesis from a validated pattern.

日本語の概要は準備中です。原文の説明を表示しています。

Biological data management and lineage tracking with LaminDB

日本語の概要は準備中です。原文の説明を表示しています。

Query BindingDB for protein-ligand binding affinity data

日本語の概要は準備中です。原文の説明を表示しています。

Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.

日本語の概要は準備中です。原文の説明を表示しています。

Database design with schema analysis, index optimization, and migration generation for PostgreSQL, MySQL, MongoDB, and DynamoDB. Use when designing schemas, optimizing queries, planning migrations, or analyzing database performance.

日本語の概要は準備中です。原文の説明を表示しています。

A button that springs down on press and pops back — tactile tap feedback. An App-UI micro-interaction (mobile component feedback). Honors prefers-reduced-motion. Keep it short and snappy; only on controls that actually change state.

日本語の概要は準備中です。原文の説明を表示しています。

Generate a coaching conversation guide with questions, protocols, and follow-up actions for a teaching focus. Use when preparing for coaching sessions, mentoring, or peer feedback conversations.

日本語の概要は準備中です。原文の説明を表示しています。

Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.

日本語の概要は準備中です。原文の説明を表示しています。

Design a single-point rubric with one criterion and open columns for evidence. Use for student self-assessment, peer feedback, teacher formative feedback, or pre-task planning. Works with any learning target, with or without a band system.

日本語の概要は準備中です。原文の説明を表示しています。

Analyse student work against criteria to identify specific gaps between current performance and learning objectives. Use when reviewing submissions, planning feedback, or diagnosing learning needs.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for simulation and tycoon games (SimCity, RollerCoaster Tycoon, Factorio, Two Point Hospital) covering economy management, time progression, interconnected systems, NPC simulation, and feedback loops. Use when building management sims, tycoon games, city builders, or resource optimization games. Keywords tycoon, economy system, resource management, time scale, feedback loop, progression unlock, simulation tick.

日本語の概要は準備中です。原文の説明を表示しています。

Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for simulation and tycoon games (SimCity, RollerCoaster Tycoon, Factorio, Two Point Hospital) covering economy management, time progression, interconnected systems, NPC simulation, and feedback loops. Use when building management sims, tycoon games, city builders, or resource optimization games. Keywords tycoon, economy system, resource management, time scale, feedback loop, progression unlock, simulation tick.

日本語の概要は準備中です。原文の説明を表示しています。

Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.

日本語の概要は準備中です。原文の説明を表示しています。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

日本語の概要は準備中です。原文の説明を表示しています。